The invention relates to the spatially resolved determination of the nature and state of a tissue from spatially resolved mass spectra of a tissue section. Histology is the science of human, animal and plant tissues, in particular their structure and function. A histological classification of a tissue is equivalent to a determination of the nature and state of the tissue, which can refer to the type and differentiations of the tissue, bacterial and parasitic pathogens in the tissue, the disease status of the tissue or any other change compared to a normal state. In the following, the term “histology” also includes the examination of tissue in order to study manifestations of disease (“histopathology”). The diseases of a tissue relate to inflammatory diseases, metabolic diseases and the detection of tumors, especially the differentiation between benign and malignant forms of tumor.
In a routine histological examination, the nature and state of the tissue are determined via optical images of tissue sections, obtained by microscopes or scanners. Usually, the tissue sections are only a few micrometers thick and are stained in order to increase the contrast in the optical images and emphasize structures in the tissue sections. Histology has so far been mainly a morphologic diagnostic method because the tissue's nature and state are determined according to the appearance and staining properties of the tissue and cell structures. Since normally the method produces an image of a complete tissue section, usually the tissue states at different positions of the tissue section are determined with some spatial resolution.
The nature and state of a tissue can also be indicated at a molecular level as concentration patterns of biological substances such as proteins, nucleic acids, lipids or sugars. A molecular pattern may show that biological substances are underexpressed or overexpressed in certain tissue areas. Proteins, in particular, may also be modified in characteristic ways, e.g. by posttranslational modifications. In recent years, the search for substances which are characteristic of diseases (so-called biomarkers) has developed into a prominent field of clinical research. Usually the biological substances in body fluids (e.g. blood, urine or spinal fluid) or homogenized tissue samples are separated into fractions, typically by solid phase extraction or chromatographic separation methods, followed by mass spectrometric analysis. The measured mass spectra exhibit signal patterns with varying degrees of complexity, which usually originates from peptides, proteins and lipids.
Mass spectra of a tissue sample without prior separation into fractions contain a large amount of molecular information with a multitude of signals. The nature and state of the tissue is generally determined not just by an individual signal, but by a pattern of different signals. There are a multitude of different classification algorithms derived from mathematical statistics which can be used to determine the state of a tissue sample from the high-dimensional signal pattern of its measured mass spectrum, e.g. neuronal networks (Linear Vector Quantization (LVQ), Neural Gas (NG), Self-Organizing Map (SOM)), Support Vector Machines (SVM), genetic algorithms for cluster analysis, Principal Component Analysis (PCA), decision trees or nearest neighbor classification (k-Nearest-Neighbor).
Before the tissue state of a tissue sample can be determined with a classification algorithm, mass spectra of a large number of tissue samples, e.g. of healthy and sick individuals, are first measured and analyzed to ascertain whether the classification algorithm can in fact be used to differentiate between classes, and thus tissue states, using the measured mass spectrometric input data. If there are parameters or parameter intervals for the classification algorithm which allow tissue states to be distinguished in a statistically significant way in the input data, these parameters can be used to assign mass spectra of other tissue samples to one of the classes, and to thus determine the tissue states of these other tissue samples.
Some of the classification algorithms permit automatically determining which signals of the mass spectra are most relevant for a classification, thus reducing the mass spectra to the relevant mass intervals. For example, Principal Component Analysis is used to bring about a reduction of mass intervals to those mass intervals whose signals have the largest influence on the variance of the high-dimensional signal patterns and thus often have the highest information content. In contrast, for “supervised” classification algorithms, such as Support Vector Machines, it is necessary to assign each of the mass spectra used as input data to a class (e.g. diseased or healthy) in the teaching phase, i.e. these “training spectra” carry a label.
In recent years, “imaging mass spectrometry” (IMS) increasingly has been used to analyze histological tissue sections pixel-wise with thousands of spatially resolved mass spectra instead of acquiring spectra of homogenized tissue samples, preferably with MALDI time-of-flight mass spectrometers (MALDI=ionization by matrix assisted laser desorption). Documents U.S. Pat. Nos. 7,667,196 B2 (DE 10 2006 019 530 B4) and 2008/0142703 A1 (DE 10 2006 059 695 B3) (M. Schürenberg et al., 2006) elucidate different methods and devices which can be used to prepare tissue sections on MALDI sample supports. This involves applying a matrix solution in the form of small droplets to a tissue section by vibrational nebulizing, for example, where the solution vaporizes and the matrix substance crystallizes together with the substances extracted from the tissue section.
A raster scan method according to Caprioli (U.S. Pat. No. 5,808,300 A) is usually used to measure, pixel by pixel, spatially resolved MALDI mass spectra. However, partial regions of the tissue section can also be imaged using ion optics (Luxembourg et al., Analytical Chemistry, 76 (18), 2004, 5339-5344: “High-Spatial Resolution Mass Spectrometric Imaging of Peptide and Protein Distributions on a Surface”). In both cases a corresponding mass image of the tissue section results from the signals of each mass interval that is resolved in the mass spectra. The molecular information of the tissue section is present in a spatially resolved form.
According to the document U.S. Pat. No. 7,873,478 B2 (equivalent to GB 2 418 773 B and DE 10 2004 037 512 A1, D. Suckau et al.), mass spectra for a tissue section are measured with spatial resolution, and from each of the spatially resolved mass spectra for each pixel, a tissue state is calculated at the corresponding pixel position of the tissue section. This method is not used to determine a tissue state of a (homogenized) tissue sample, but a status image of the tissue section. The spatially resolved tissue states, as pixels of the status image, are calculated with the above-mentioned classification algorithms derived from mathematical statistics. The information from the large number of measured mass images of the tissue section is summarized in a single status image and is thus presented in a graphic form easily understandable by the user.
The spatially resolved mass spectra of the tissue section under analysis can themselves serve here as input data for the classification algorithm used. This involves selecting spatially resolved mass spectra of a partial region in order to set the parameters of the classification algorithm before the tissue states in other regions are determined. However, the parameters of the classification algorithm can also exist as parameters that have already been evaluated in previous analyses of spatially resolved mass spectra of other tissue sections or of mass spectra from homogenized tissue samples.
In imaging mass spectrometry which uses MALDI ion sources, the spatial resolution (the spatial resolving power) is limited by the application of the matrix layer and its effect on the sample under investigation. In the preparation of tissue sections, the spatial resolution in the mass images is currently between ten and one hundred micrometers. It is therefore not possible to resolve any structures which are smaller than around 5 micrometers in the mass images of tissue sections measured in this way. The spatial resolution is more than an order of magnitude worse than that of the light-optical images of a tissue section. It is no trivial task to apply the matrix layer to the tissue section because (a) a lateral smearing of the biological substances must be avoided, (b) the biological substances must be extracted from the tissue section and incorporated into the crystals of the matrix layer, and (c) a favorable ratio of biologically relevant substances to impurities must be achieved.
The status images of tissue sections calculated according to the prior art have a low signal-to-noise ratio, so structures can often not be discerned sufficiently. Furthermore, the accuracy of classifying tissue states in the status images is lower than with classification from homogenized tissue samples. Different types of classification error occur with every type of classification, including the determination of tissue states. These errors result in statistical parameters which determine the quality of the classification. The parameters include the sensitivity (true positive rate), the specificity (true negative rate), the false positive rate (false alarm) and the false negative rate (undetected case).